library(tidyverse)
library(targets)
library(kableExtra)

1 Leaf disc vs whole-leaf LMA (species-level)

1.1 Pooled

1.2 Seprated

1.3 MCMC check

targets::tar_load("fit_sp_0_summary_simple")
targets::tar_load("fit_sp_1_summary_model")
targets::tar_load("fit_sp_2_summary_model")
targets::tar_load("fit_sp_3_summary_model")
targets::tar_load("fit_sp_4_summary_sma")
# targets::tar_load("fit_sp_5_summary_sma")
targets::tar_load("fit_sp_6_summary_sma_err")

targets::tar_load("fit_sp_0_diagnostics_simple")
targets::tar_load("fit_sp_1_diagnostics_model")
targets::tar_load("fit_sp_2_diagnostics_model")
targets::tar_load("fit_sp_3_diagnostics_model")
targets::tar_load("fit_sp_4_diagnostics_sma")
# targets::tar_load("fit_sp_5_diagnostics_sma")
targets::tar_load("fit_sp_6_diagnostics_sma_err")
fit_sp_0_summary_simple |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> #   mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> #   .join_data <dbl>
fit_sp_1_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> #   mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> #   .join_data <dbl>
fit_sp_2_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> #   mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> #   .join_data <dbl>
fit_sp_3_summary_model |> filter(rhat > 1.1)
#> # A tibble: 0 × 11
#> # … with 11 variables: variable <chr>, mean <dbl>, median <dbl>, sd <dbl>,
#> #   mad <dbl>, q5 <dbl>, q95 <dbl>, rhat <dbl>, ess_bulk <dbl>, ess_tail <dbl>,
#> #   .join_data <dbl>
fit_sp_4_summary_sma |> filter(rhat > 1.1)
#> # A tibble: 29 × 11
#>    variable        mean   median      sd     mad       q5     q95  rhat ess_bulk
#>    <chr>          <dbl>    <dbl>   <dbl>   <dbl>    <dbl>   <dbl> <dbl>    <dbl>
#>  1 lp__        497.     428.     1.85e+2 1.38e+2  3.15e+2 9.00e+2  1.14     20.8
#>  2 sigma         0.0927   0.100  3.82e-2 4.46e-2  2.46e-2 1.41e-1  1.14     20.9
#>  3 sigma_x_un…   0.0386   0.0420 1.81e-2 2.09e-2  5.48e-3 6.13e-2  1.13     23.3
#>  4 log_lma_di…   4.33     4.32   1.47e-1 2.06e-1  4.12e+0 4.54e+0  1.10     28.1
#>  5 log_lma_di…   4.03     4.04   1.85e-1 2.62e-1  3.75e+0 4.28e+0  1.12     24.1
#>  6 log_lma_di…   3.97     3.97   1.82e-1 2.59e-1  3.70e+0 4.22e+0  1.10     28.9
#>  7 log_lma_di…   3.68     3.68   1.65e-1 2.35e-1  3.45e+0 3.93e+0  1.10     27.9
#>  8 log_lma_di…   3.93     3.93   1.58e-1 2.20e-1  3.71e+0 4.17e+0  1.12     25.0
#>  9 sigma_x       0.0967   0.105  4.52e-2 5.25e-2  1.37e-2 1.53e-1  1.13     23.3
#> 10 log_lik[12]  -0.463   -0.574  1.88e+0 2.36e+0 -3.16e+0 2.51e+0  1.11     26.8
#> # … with 19 more rows, and 2 more variables: ess_tail <dbl>, .join_data <dbl>
# fit_sp_5_summary_sma |> filter(rhat > 1.1)
fit_sp_6_summary_sma_err |> filter(rhat > 1.1)
#> # A tibble: 2,305 × 11
#>    variable       mean   median      sd     mad       q5      q95  rhat ess_bulk
#>    <chr>         <dbl>    <dbl>   <dbl>   <dbl>    <dbl>    <dbl> <dbl>    <dbl>
#>  1 lp__        4.68e+2  4.86e+2 1.37e+2 1.73e+2  2.29e+2 630.      1.59     7.38
#>  2 beta[1]     9.18e-2  9.02e-2 6.09e-3 4.62e-3  8.25e-2   0.103   1.31    76.9 
#>  3 beta[2]    -3.03e-2 -2.95e-2 8.45e-3 8.71e-3 -4.57e-2  -0.0199  1.16    18.6 
#>  4 beta[3]     3.19e-2  3.09e-2 6.73e-3 6.61e-3  2.23e-2   0.0431  1.13    20.6 
#>  5 beta[4]     2.11e-3  1.84e-3 6.58e-3 4.64e-3 -9.85e-3   0.0133  1.47   123.  
#>  6 gamma[1]   -2.90e+0 -2.92e+0 3.96e-1 5.08e-1 -3.40e+0  -2.20    1.58     7.41
#>  7 gamma[2]   -2.22e-1 -2.10e-1 1.22e-1 1.42e-1 -4.48e-1  -0.0897  1.31    10.6 
#>  8 gamma[3]    4.30e-1  4.82e-1 1.80e-1 1.71e-1  1.25e-1   0.640   1.38     8.92
#>  9 gamma[4]   -3.73e-1 -3.07e-1 2.76e-1 3.37e-1 -7.47e-1  -0.0291  1.53     7.37
#> 10 sigma_x_u…  3.55e-2  3.90e-2 9.91e-3 6.23e-3  1.27e-2   0.0439  1.52     7.49
#> # … with 2,295 more rows, and 2 more variables: ess_tail <dbl>,
#> #   .join_data <dbl>

It is difficult to model heteroskedasticity and measurement errors at the same time.

div_check(fit_sp_0_diagnostics_simple)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_1_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_2_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_3_diagnostics_model)
#> [1] "0 of 8000 iterations ended with a divergence 0 %"
div_check(fit_sp_4_diagnostics_sma)
#> [1] "645 of 16000 iterations ended with a divergence 4.03125 %"
# div_check(fit_sp_5_diagnostics_sma)
div_check(fit_sp_6_diagnostics_sma_err)
#> [1] "3169 of 8000 iterations ended with a divergence 39.6125 %"
targets::tar_load(loo_model)
loo::loo_compare(loo_model[[1]], loo_model[[2]], loo_model[[3]], loo_model[[4]])
#>        elpd_diff se_diff
#> model4  0.0       0.0   
#> model3 -0.4       0.4   
#> model1 -0.4       0.5   
#> model2 -9.9       5.6
tar_read(cv_sp)
#> $table
#> # A tibble: 4 × 3
#>   model    r2    mse
#>   <chr> <dbl>  <dbl>
#> 1 fit 1 0.866 0.0195
#> 2 fit 2 0.866 0.0196
#> 3 fit 3 0.867 0.0194
#> 4 fit 4 0.874 0.0183
#> 
#> $fit1
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#> 
#> Coefficients:
#>   (Intercept)  log(lma_disc)  
#>        0.4852         0.9108  
#> 
#> 
#> $fit2
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree, 
#>     offset = log(lma_disc))
#> 
#> Coefficients:
#> (Intercept)      log(la)      log(lt)  
#>    0.007418     0.036529     0.028880  
#> 
#> 
#> $fit3
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt), 
#>     data = tree, offset = log(lma_disc))
#> 
#> Coefficients:
#>  (Intercept)  log(ld_leaf)       log(la)       log(lt)  
#>      0.09834       0.06599       0.03733       0.04426  
#> 
#> 
#> $fit4
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt), 
#>     data = tree)
#> 
#> Coefficients:
#>   (Intercept)  log(lma_disc)        log(la)        log(lt)  
#>       0.64625        0.88001        0.03052        0.10016

1.4 coef

tar_read(coef_sp_plot)

tar_load(coef_sp_png2)

tar_load(coef_sp_png3)

2 SMA table

2.1 species

tar_load(sma_sp_tab)
sma_sp_tab |>
  kable() |>
  kable_styling()
Data Slope Intercept R2
All 0.97 [0.94, 1.01] 0.09 [0.02, 0.16] 0.87
Thick~Large 0.93 [0.85, 1.03] 0.19 [0.01, 0.37] 0.86
Thick~Small 1.02 [0.97, 1.07] 0 [-0.11, 0.11] 0.93
Thin~Large 0.91 [0.82, 1.02] 0.2 [0.02, 0.37] 0.71
Thin~Small 0.93 [0.83, 1.03] 0.16 [-0.02, 0.35] 0.84

2.2 species LD

tar_load(sma_sp_ld_tab)
sma_sp_ld_tab |>
  kable() |>
  kable_styling()
Data Slope Intercept R2
All 0.97 [0.94, 1.01] 0.09 [0.02, 0.16] 0.87
Dense ~ Thick~Large 0.96 [0.82, 1.13] 0.14 [-0.15, 0.43] 0.79
Dense ~ Thick~Small 1.02 [0.94, 1.11] -0.01 [-0.17, 0.15] 0.90
Dense ~ Thin~Large 0.8 [0.66, 0.98] 0.37 [0.09, 0.64] 0.60
Dense ~ Thin~Small 0.86 [0.72, 1.03] 0.27 [-0.01, 0.55] 0.81
Nondense ~ Thick~Large 0.75 [0.59, 0.94] 0.58 [0.22, 0.94] 0.72
Nondense ~ Thick~Small 0.99 [0.87, 1.13] 0.05 [-0.23, 0.32] 0.82
Nondense ~ Thin~Large 0.77 [0.65, 0.91] 0.49 [0.25, 0.74] 0.56
Nondense ~ Thin~Small 0.87 [0.71, 1.06] 0.28 [-0.06, 0.62] 0.68

2.3 tree

tar_load(sma_tree_tab)
sma_tree_tab |>
  kable() |>
  kable_styling()
Data Slope Intercept R2
All 0.96 [0.94, 0.99] 0.11 [0.06, 0.16] 0.72
Thick~Large 0.97 [0.9, 1.04] 0.12 [-0.03, 0.26] 0.61
Thick~Small 0.94 [0.9, 0.98] 0.16 [0.07, 0.24] 0.79
Thin~Large 0.91 [0.84, 0.97] 0.21 [0.09, 0.32] 0.45
Thin~Small 0.91 [0.85, 0.98] 0.19 [0.07, 0.31] 0.66

2.4 tree LD

tar_load(sma_tree_ld_tab)
sma_tree_ld_tab |>
  kable() |>
  kable_styling()
Data Slope Intercept R2
All 0.96 [0.94, 0.99] 0.11 [0.06, 0.16] 0.72
Dense ~ Thick~Large 0.89 [0.79, 1] 0.25 [0.06, 0.44] 0.49
Dense ~ Thick~Small 0.91 [0.85, 0.97] 0.21 [0.1, 0.32] 0.76
Dense ~ Thin~Large 0.75 [0.67, 0.85] 0.44 [0.29, 0.6] 0.34
Dense ~ Thin~Small 0.8 [0.72, 0.9] 0.36 [0.2, 0.52] 0.65
Nondense ~ Thick~Large 0.74 [0.64, 0.86] 0.61 [0.38, 0.83] 0.43
Nondense ~ Thick~Small 0.84 [0.78, 0.91] 0.38 [0.24, 0.53] 0.68
Nondense ~ Thin~Large 0.73 [0.65, 0.81] 0.57 [0.42, 0.72] 0.28
Nondense ~ Thin~Small 0.89 [0.78, 1.01] 0.25 [0.03, 0.46] 0.38

3 Validation

tar_load(cv_sp)
cv_sp
#> $table
#> # A tibble: 4 × 3
#>   model    r2    mse
#>   <chr> <dbl>  <dbl>
#> 1 fit 1 0.866 0.0195
#> 2 fit 2 0.866 0.0196
#> 3 fit 3 0.867 0.0194
#> 4 fit 4 0.874 0.0183
#> 
#> $fit1
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#> 
#> Coefficients:
#>   (Intercept)  log(lma_disc)  
#>        0.4852         0.9108  
#> 
#> 
#> $fit2
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree, 
#>     offset = log(lma_disc))
#> 
#> Coefficients:
#> (Intercept)      log(la)      log(lt)  
#>    0.007418     0.036529     0.028880  
#> 
#> 
#> $fit3
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt), 
#>     data = tree, offset = log(lma_disc))
#> 
#> Coefficients:
#>  (Intercept)  log(ld_leaf)       log(la)       log(lt)  
#>      0.09834       0.06599       0.03733       0.04426  
#> 
#> 
#> $fit4
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt), 
#>     data = tree)
#> 
#> Coefficients:
#>   (Intercept)  log(lma_disc)        log(la)        log(lt)  
#>       0.64625        0.88001        0.03052        0.10016
tar_load(cv_tree)
cv_tree
#> $table
#> # A tibble: 4 × 3
#>   model    r2    mse
#>   <chr> <dbl>  <dbl>
#> 1 fit 1 0.718 0.0421
#> 2 fit 2 0.684 0.0467
#> 3 fit 3 0.702 0.0440
#> 4 fit 4 0.739 0.0385
#> 
#> $fit1
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc), data = tree)
#> 
#> Coefficients:
#>   (Intercept)  log(lma_disc)  
#>        0.8911         0.8176  
#> 
#> 
#> $fit2
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(la) + log(lt), data = tree, 
#>     offset = log(lma_disc))
#> 
#> Coefficients:
#> (Intercept)      log(la)      log(lt)  
#>     0.05804      0.03071      0.05749  
#> 
#> 
#> $fit3
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(ld_leaf) + log(la) + log(lt), 
#>     data = tree, offset = log(lma_disc))
#> 
#> Coefficients:
#>  (Intercept)  log(ld_leaf)       log(la)       log(lt)  
#>      0.30044       0.16541       0.03573       0.11701  
#> 
#> 
#> $fit4
#> 
#> Call:
#> lm(formula = log(lma_leaf) ~ log(lma_disc) + log(la) + log(lt), 
#>     data = tree)
#> 
#> Coefficients:
#>   (Intercept)  log(lma_disc)        log(la)        log(lt)  
#>       1.54932        0.72303        0.01379        0.21886

4 Leaf disc vs whole-leaf LMA (individual-level)

5 Divergence (species-level)

6 LMA and LD (species-level)

7 CV

8 Leaf support cost (species-level)

9 Computing Environment

devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#>  setting  value
#>  version  R version 4.1.3 (2022-03-10)
#>  os       Ubuntu 20.04.4 LTS
#>  system   x86_64, linux-gnu
#>  ui       X11
#>  language (EN)
#>  collate  en_US.UTF-8
#>  ctype    en_US.UTF-8
#>  tz       Etc/UTC
#>  date     2022-05-02
#>  pandoc   2.16.2 @ /usr/bin/ (via rmarkdown)
#> 
#> ─ Packages ───────────────────────────────────────────────────────────────────
#>  ! package        * version    date (UTC) lib source
#>  P abind            1.4-5      2016-07-21 [?] RSPM (R 4.1.0)
#>  P assertthat       0.2.1      2019-03-21 [?] RSPM (R 4.1.3)
#>    backports        1.4.1      2021-12-13 [1] RSPM (R 4.1.3)
#>  P base64url        1.4        2018-05-14 [?] RSPM (R 4.1.0)
#>  P broom            0.7.12     2022-01-28 [?] RSPM (R 4.1.3)
#>  P bslib            0.3.1      2021-10-06 [?] RSPM (R 4.1.3)
#>  P cachem           1.0.6      2021-08-19 [?] RSPM (R 4.1.0)
#>  P callr            3.7.0      2021-04-20 [?] CRAN (R 4.1.1)
#>  P car              3.0-12     2021-11-06 [?] RSPM (R 4.1.0)
#>  P carData          3.0-5      2022-01-06 [?] RSPM (R 4.1.0)
#>  P cellranger       1.1.0      2016-07-27 [?] RSPM (R 4.1.3)
#>  P checkmate        2.0.0      2020-02-06 [?] RSPM (R 4.1.0)
#>  P cli              3.2.0      2022-02-14 [?] RSPM (R 4.1.0)
#>  P cmdstanr       * 0.5.1.9000 2022-04-19 [?] Github (stan-dev/cmdstanr@bfa83a4)
#>  P codetools        0.2-18     2020-11-04 [?] CRAN (R 4.1.2)
#>  P colorspace       2.0-3      2022-02-21 [?] RSPM (R 4.1.3)
#>    crayon           1.5.1      2022-03-26 [1] RSPM (R 4.1.3)
#>  P data.table       1.14.2     2021-09-27 [?] RSPM (R 4.1.3)
#>  P DBI              1.1.2      2021-12-20 [?] RSPM (R 4.1.3)
#>  P dbplyr           2.1.1      2021-04-06 [?] RSPM (R 4.1.3)
#>  P desc             1.4.0      2021-09-28 [?] CRAN (R 4.1.1)
#>  P devtools         2.4.3      2021-11-30 [?] RSPM (R 4.1.0)
#>    digest           0.6.29     2021-12-01 [1] RSPM (R 4.1.3)
#>  P distributional   0.3.0      2022-01-05 [?] RSPM (R 4.1.0)
#>  P dplyr          * 1.0.8      2022-02-08 [?] RSPM (R 4.1.3)
#>  P ellipsis         0.3.2      2021-04-29 [?] CRAN (R 4.1.1)
#>    evaluate         0.15       2022-02-18 [1] RSPM (R 4.1.3)
#>  P extrafont      * 0.17       2014-12-08 [?] RSPM (R 4.1.0)
#>  P extrafontdb      1.0        2012-06-11 [?] RSPM (R 4.1.0)
#>    fansi            1.0.3      2022-03-24 [1] RSPM (R 4.1.3)
#>  P farver           2.1.0      2021-02-28 [?] RSPM (R 4.1.3)
#>  P fastmap          1.1.0      2021-01-25 [?] CRAN (R 4.1.1)
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#>    fs               1.5.2      2021-12-08 [1] RSPM (R 4.1.3)
#>  P fst              0.9.8      2022-02-08 [?] RSPM (R 4.1.0)
#>  P fstcore        * 0.9.12     2022-03-23 [?] RSPM (R 4.1.0)
#>  P generics         0.1.2      2022-01-31 [?] RSPM (R 4.1.3)
#>  P ggplot2        * 3.3.5      2021-06-25 [?] RSPM (R 4.1.3)
#>  P ggpubr         * 0.4.0.999  2022-04-09 [?] Github (mattocci27/ggpubr@901fdeb)
#>  P ggsignif         0.6.3      2021-09-09 [?] RSPM (R 4.1.0)
#>  P ggsma          * 0.1.0      2022-04-09 [?] Github (mattocci27/ggsma@2b56c57)
#>  P glue             1.6.2      2022-02-24 [?] RSPM (R 4.1.0)
#>  P gtable           0.3.0      2019-03-25 [?] RSPM (R 4.1.3)
#>  P haven            2.4.3      2021-08-04 [?] RSPM (R 4.1.3)
#>  P highr            0.9        2021-04-16 [?] CRAN (R 4.1.1)
#>  P hms              1.1.1      2021-09-26 [?] RSPM (R 4.1.3)
#>  P htmltools        0.5.2      2021-08-25 [?] CRAN (R 4.1.1)
#>  P httr             1.4.2      2020-07-20 [?] CRAN (R 4.1.2)
#>  P igraph           1.3.0      2022-04-01 [?] RSPM (R 4.1.0)
#>  P insight          0.17.0     2022-03-29 [?] RSPM (R 4.1.0)
#>  P janitor        * 2.1.0      2021-01-05 [?] RSPM (R 4.1.0)
#>  P jquerylib        0.1.4      2021-04-26 [?] RSPM (R 4.1.3)
#>  P jsonlite         1.7.2      2020-12-09 [?] CRAN (R 4.1.1)
#>  P kableExtra     * 1.3.4      2021-02-20 [?] RSPM (R 4.1.0)
#>    knitr          * 1.38       2022-03-25 [1] RSPM (R 4.1.3)
#>  P labeling         0.4.2      2020-10-20 [?] RSPM (R 4.1.3)
#>  P languageserver * 0.3.12     2021-10-19 [?] RSPM (R 4.1.0)
#>  P lifecycle        1.0.1      2021-09-24 [?] CRAN (R 4.1.1)
#>  P loo            * 2.5.1      2022-03-24 [?] RSPM (R 4.1.0)
#>  P lubridate        1.8.0      2021-10-07 [?] RSPM (R 4.1.3)
#>    magrittr         2.0.3      2022-03-30 [1] RSPM (R 4.1.3)
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